How has COVID-19 affected the US substance abuse crisis?

Introduction

Drug overdose deaths in the United States have been rising steadily since the turn of the century, and a significant increase in this trend has been observed since the mid-2010’s. Public discourse around this tragedy led to cultural and political changes which appeared to have slowed the trend around 2018. Then, in March 2020, the COVID-19 pandemic led to an upheaval in nearly every aspect of daily life, resulting in drastic changes to the way we work, socialize, and interact with society at large.

Such a fundamental change in the way we live our lives led to universally destabilizing experiences. To slow the spread of the virus, most public places of congregation were shut down, leading to widespread loss of jobs and a crash of the economy. Those with job security soon found new ways to work through the pandemic, and those without it found themselves without a job and an income.

The category of person most likely to be debilitated by the COVID-19 pandemic correlates with the type of person most vulnerable to experiencing drug addiction. Substance abuse is associated with unemployment or underemployment, lack of career opportunities, social isolation, mental health issues, and homelessness. As COVID-19 has undeniably contributed to each of these factors, an investigation into the pandemic’s effect on the substance abuse crisis is warranted.

Preliminary Results

A timeline:

COVID-19 was first detected in the USA on January 17, 2020, in Washington State. By March 13, 2020, President Trump had declared a nationwide emergency, and 2 days later, schools and restaurants began to shut down. By May 9th, 2020, the unemployment rate hit 14.7%, the worst rate since the Great Depression. By September 2020, the US COVID-19 death toll surpassed 200,000, and by January 18, 2021, it had doubled to 400,000. On December 14th, 2020, the initial phase of the vaccination program began, and by March 13, 2021, the US had surpassed 100 million vaccinations administered. By July 1st, 2021, the delta variant had become detected in all 50 US States.

This timeline helps provide a framework for interpreting the following simple plots:

There has been considerable difference between how each of the US States has experienced COVID-19:

The state with the highest number deaths per population, MISSISSIPPI, experienced per population mortality rates 555% higher than that of the lowest number of deaths per population, VERMONT.

These differences are a result of a complicated web of interconnected variables: geography, viral transmission tendencies, the extent to which the state and local governments enforced lockdown measures, the culture of the communities and how rigorously they practiced social distancing, the types of economies typical of each region, and many more.

Having the above information in hand will help provide perspective as a preliminary investigation into US drug useage patterns is explored.

The drug mortality crisis in the US

The CDC provides a vast and thorough dataset giving monthly 12-month rolling summaries of overdose deaths, by state, categorized by drug type (heroin, cocaine, opioids, etc.) as well as drug type subcategories (synthetic opioids, natural & semi-synthetic opioids, etc.):

(Note: No data regarding specific drug types is available for the years of 2016 and 2017. I’m searching for a way to fill in this data)

Zooming in on the most telling parts of this graph:

Shown below is overdoses in the US from 2015-2021, broken down by drug type and subtype. Some of these are independent of all others: Psychostimulants (methamphedamine) and cocaine have their own categories. Others share data: there is one category for all opioids, another for natural opioids, and another for synthetic opioids.

The following graph immediately suggests some insights. For one, there does appear to be a clear acceleration in the rate of drug overdose deaths following the onset of the COVID-19 pandemic. Another is regarding the types of drugs involved: It is opioids that are by far the most implicated drug, and it’s the synthetic, not the natural opioids, that are the primary driver. Natural opioids are those such as morphine and codeine; common semi-synthetic opioids are hydrocodone and oxycodone.

The synthetic opioid behind this great increase is fentanyl, which is often illicitly manufactured and commonly used to lace other drugs. There has been considerable media coverage of the rise of fentanyl and the damage it is causing the US, and many government agencies have declared an “opioid epidemic.”

In January 2015 - the earliest data provided in this dataset - synthetic opioids accounted for 12.133% of all overdose deaths. As of March 01, 2021 - the most recent data provided in this dataset - synthetic opioids were responsible for 63.617% of all drug overdose deaths in the US. During this time, the total number of deaths due to synthetic opioids grew by 1081.131%.

Similar to the disparity in how each state experienced the COVID-19 pandemic, each state is experiencing the drug epidemic in its own way:

The reason why states are experiencing the drug overdose epidemic so differently begs further investigation. Is there a relationship with the COVID-19 pandemic?

Here are two ways of probing this question with graphs:

Plotting to investigate whether those states which experienced the most COVID-19 cases per capita also experienced the greatest relative increase in deaths due to overdose in the same time period,

OR

Similarly, whether COVID-19 deaths per capita is correlated with all overdose deaths in the same timeframe, per capita:

These visualizations indicate that there is either no relation between the two factors, or there is even a negative correlation.

Consider geographical distribution of these factors:

While this may seem counterintuitive, COVID-19 affected the US in more ways than just the mortalities from the virus itself. The economic shutdown and enforced social distancing changed the way we live our entire lives, and these are the types of disruptions that could lead to increased drug usage: social isolation, joblessness, and the associated mental health effects. One conjecture which might explain the lack of correlation seen above is that those states which experienced the lowest COVID-19 mortalities perhaps enforced the most severe lockdowns - resulting in lower virus associated deaths but higher social isolation and a more damaged economy.

Conclusion

This investigation has only suggested more directions of research that should be pursued. I would have liked to do more preliminary investigating of the regional characteristics of both the COVID-19 and drug overdose datasets using the library leaflet, but it was out of the scope of this report. I would like to merge economic data into this analysis, allowing me to consider the degree to which state economies suffered from the COVID-19 associated shutdowns, to see if interesting correlations with drug usage can be discovered. I am interested in finding more spatially precise datasets, perhaps at the county level, as each state contains a multitude of regions which act independently of one another. I feel that this topic is an important and interesting one with many insights left to discover, and I intend to continue pursuing it.

Methods

Data regarding the population of the US and its States was collected from the census.gov’s API portal. It was converted to a data.table by the methods instructed in lecture, and associated with other data.tables using the data.table merge method. The COVID-19 data was accessed via the data.cdc.gov API portal. It was merged with census data to calculate COVID-19 infections and deaths as a percentage of the state population. Data regarding drug overdoses in the USA was also accessed through the data.cdc.gov API. It was merged with the census data to be able to calculate overdose percentages by state population. Later, it was merged with the COVID-19 data to investigate the relationship between overdoses and COVID-19 infection rates. The data was very reliable, as it was thoroughly gathered by the CDC, and needed very little cleaning or wrangling. Data exploration was mainly done visually using ggplot2 line and scatterplots, most of which are shown in this report.